Artificial Intelligence in Financial Lending

January 7, 2018
| 262 views

I remember the 90s when I wanted to get a home loan and it took me 3 months to complete the process from providing all the hard copies of my income, tax returns, identity proofs then bank checked my creditworthiness & provided the approval.

Today everybody has some kind of loans like home loan, auto loan, education loan, two wheeler loan or even loan to buy appliances like HD TV and Refrigerator.

How do they assess your creditworthiness? There are so many cases of defaulters, which keeps increasing and hence established banks or lenders constantly looking for ways to improve the returns or proactively identify risks.

Lenders traditionally make decisions based on a loan applicant’scredit score, a three-digit number obtained from credit bureaus such as the TransUnion, Experian, and Equifax. But these credit scores are based solely on credit-history and do not take into account rich data available, which can potentially give lenders access to data points as varied as online purchases, the strength of social connections and travel patterns. When viewed this data holistically, lenders can get a complete picture of potential borrowers & can significantly improve their ability to predict loan defaults.

Todaydigital transformationhas changed everything. While the interest rate and closing costs on loans are still primary considerations, the speed, simplicity, transparency and customer service of the entire process is important.

As the purchasing power among millennials & gen Z continues to increase, they tend to purchase property and acquire assets that will provide stability & generate wealth.

The ability tocross-sellto these customers on loan products drives a significant portion of new loans. The difference for a digital-first customer is that they do their shopping online and may select an alternative provider based on the right combination of cost and ease of process.

Artificial Intelligenceis used today, to determine the creditworthiness of those who don’t have any credit history like students or immigrants etc. It also helps to improvecustomer experience, e.g. by showing pre-approved loan amount. AI makes loan approvals quick and easy, reduce operational costs and these savings can then be extended to customers in the form of lower rates. Artificial Intelligence can process large amounts of data that human underwriters would simply not be able to make sense of.

Machine learningstreamlines the process, drastically reduces the likelihood of errors and significantly cuts down the time it takes to approve a loan and disburse funds to the borrower, thereby enhancing the customer experience.

AI & Machine learning also helps to detect fraud by comparing customer behavior with the baseline data of normal customers and removing outliers.

Today apart from credit score and income, lenders are also looking at the digital footprint, payment data from other sources, purchase history, professional reputation from LinkedIn and other sources.

This is called alternative data sourcing. The use of machine learning to analyze this alternative data in loans and credit rating is going to raise some privacy, ethical, and legal concerns.

The future of digital lending will reduce the friction associated with the borrowing process, eliminating paperwork and moving all of the steps of the customer journey to an online and mobile capability. AI and Machine learning will become an inherent part of financial lending.